Daniel Canedo

Papers

Total Citations

H-Index

About

Daniel Canedo is a researcher at the forefront of intelligent robotics and computer vision, with a primary focus on enhancing the autonomy of domestic service robots. His work centers on developing deep learning-based vision systems that enable floor-cleaning robots to perceive and interact with their environments more intelligently. Canedo’s major contribution lies in creating robust, real-time dirt detection systems, as demonstrated in his highly cited 2021 paper, “A Deep Learning-Based Dirt Detection Computer Vision System for Floor-Cleaning Robots with Improved Data Collection,” which has garnered 19 citations. This work introduced novel data collection methods to train models that can accurately identify and localize dirt, significantly improving cleaning efficiency. His subsequent research, “An Innovative Vision System for Floor-Cleaning Robots Based on YOLOv5,” further refines these capabilities using state-of-the-art object detection architectures. By bridging the gap between advanced AI and practical household robotics, Canedo’s research directly impacts the next generation of autonomous cleaning devices, making them more responsive, efficient, and truly helpful in everyday life.